BioDT - The Recreational Model

Author

Maddalena Tigli, Joe Marsh Rossney, Christopher Andrews

Published

June 1, 2025

1. Scope of the Model

The recreational potential model (RP Model) was developed as part of the Cultural Ecosystem Services prototype Digital Twin (CES-pDT) workpackage within the BioDT project. The (CES-pDT) has two independent core models:

  • the Biodiversity model (developed by Simon Rolph and Dylan Carbone, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom): aims at estimating biodiversity levels across mammals, birds, plant and insects.

  • the Recreational Potential model (developed by Jan Dick, Chris Andrews, Maddalena Tigli, Megan Williams and Joe Marsh Rossney, UK Centre for Ecology & Hydrology, Edinburgh, United Kingdom): aims at estimating the landscapes’ capacity to provide opportunities for outdoor recreation based on varying user interests.

This report specifically documents the Recreational Potential (RP) Model, detailing its methodology, data sources, operational mechanisms, and outlining areas for future refinement and development.

2. The model

2.1. Model description

flowchart LR
 subgraph s1["compute recreational potential"]
        C[("cropping of the 4<br>components file to the<br>extent of interest")]
        E["Compute each component's<br>contribution"]
        F["Rescale to unit interval"]
  end
    A("Definition of the<br><i>persona preferences</i>") ---> E
    B("Definition of the<br><i>area of interest</i>") --> C
    C --> E
    E --> F
    F --> G["recreational potential spatRast"]
    style s1 fill:#FFF9C4
    style A color:#000000
    style B color:#000000
    style C color:#000000
    style E color:#000000
    style F color:#000000
    style G color:#000000

The RP Model uses of 4 key components, each representing a different aspect of a landscape’s recreational value:

1) the landscape component (SLSRA)

2) the natural features component (FIPS_N)

3) the infrastructure component (FIPS_I)

4) the water component (Water)

For each of the 4 components, a corresponding raster file was generated ( see [3. The underlying data] paragraph to see where they came from), covering the entirety of Scotland, which is the domain that the RP model covers at the moment. Each component’s raster file store several layers, each describing a specific aspect of that component. The four raster files form the basis for calculating the recreational potential of an area, which is the main output of the RP Model.

In order to run the RP model the user must have made 2 decisions:

  • the area of interest

  • the persona preferences

Using the 4 components raster files, the recreational potential is calculated by (1) cropping the files to the area of interest, (2) compute each components contribution, (3) re scaling to unit interval.

area of interest

defined with a bounding box generated with the terra::ext() function

persona preferences

a csv with a numeric score from 0 to 10 for each of the 87 features (one feature = one layer): 13 features for the water component, 40 features for the landscape component, 24 features for the natural component and 10 features for the infrastructure component. The csv has in the first column, the name of the features, and in the second one the name and score of each persona. (see example persona file “presets.csv”). Where a higher score means a higher interest for that specific feature.

cropping the files to the area of interest

The first action the model completes is to crop the 4 components rasters to the area of interest and it does so by using the terra package in R. The terra::vect() function is used to convert the extent object provided by the user (“area of interest”) into a vector polygon (i.e., a rectangular polygon bounding box), and then the terra::crop() function to reduce the size of the raster to that of the vector polygon, trimming the rasters into the desired area of interest.

compute each component’s contribution

For each of the 4 components rasters, the model extracts the values of all its layers for each cell (20m x 20m) and multiplies them (if not NAs) with the corresponding score provided by the user (“persona preference”), the, it sums the values of that cell throughout the layers. In other words, the model computes a weighted sum of the raster layers at each cell, returning a new single layer raster with the result.

rescale to unit interval

Each of the components contribution rasters is then re-scaled so that its values are between 0 and 1 using a “min-max normalization”:

\[ scaled.value = (x-min)/mix-min \]

So the minimum values get assigned a 0, the maximum values get assigned a 1 and all other values are proportionally in between.

Them the 4 components re-scaled rasters are summed (each cell’s value is summed with its respective cell of the other components) and the final raster re-scaled once again using the “min-max normalization”.

recreational potential spatRast

The model’s output is a Spat raster with 5 layers. The first 4 layers show the scores for each component: SLSRA, FIPS_N, FIPS_I, Water, and the 5th layer is that of the combined Recreational_Potential.

2.1. Example run

Here we show examples of model output for 2 different and “opposite” personas (a hard recreationalist and a soft recreationalist) fort the Bush area. From the maps it is possible to observe how the model estimates different areas of higher recreational potential for the two personas. Moreover, it is possible to observe how each of the components contributes to the RP.

Hard recreationalist

Here we show a model run the Bush Estate area using a “hard recreationalist” example of persona (saved as “Hard_Recreationalist” in the persona presets in the model’s package). In the tables below it is possible to see which features had the highest and lowest score for this persona.

Features scored the highest and the lowest in the hard recreationalist persona
score_group features
highest scores (scored 10 or 9) Rock Walls (FIPS_N), Mountains (FIPS_N), Inland cliffs, rock pavements and outcrops (SLSRA), Rock cliffs, ledges and shores (SLSRA), National Park (SLSRA)
lowest scores (scored 0 or 1) Built-up areas (FIPS_N), Flood plain (FIPS_N), Depressions (FIPS_N), No slope (FIPS_N), Gentle slope (FIPS_N), Country Park (SLSRA), Raised and blanket bog (SLSRA), Valley mires, poor fens and transition mires (SLSRA), Windthrown woodland (SLSRA), Woodland fringes and clearings and tall forb stands (SLSRA), Bare field or exposed soil (SLSRA), Built-up area (SLSRA), Royal Society for the Protection of Birds (RSPB) Reserve (SLSRA), Pond (Water), Motorway (FIPS_I), A Road (FIPS_I), B Road (FIPS_I), Minor or local road (FIPS_I), Access roads or Track (FIPS_I), Saltings (FIPS_N)

The hard recreationalist for this example highly values rock walls, mountains, inland and rock cliffs, national parks and traffic free areas, whilst are of less interest features like built-up areas, motorways etc.

Soft recreationalist

Here we show a model run the Bush Estate area using a “soft recreationalist” example of persona (saved as “Soft_Recreationalist” in the persona presets in the model’s package). In the tables below it is possible to see which features had the highest and lowest score for this persona.

The soft recreationalist for this example highly values traffic free areas, coastal features (beaches, dunes), lochs and national parks, whilst are of less interest features like rock walls, buil-up areas, steep slopes etc.

Loading persona 'Soft_Recreationalist' from file 'C:/Users/madtig/AppData/Local/R/win-library/4.4/biodt.recreation/extdata/personas/presets.csv'
Selected an area of 3.1e+07 m^2 .

2.3. The underlying data

The methodology used to create each of the 4 components’ raster file is described in detail in this section.

Description of the 4 components’ raster files.
name raster description
SLSRA.tif

Landscape component

This includes data on land cover type, landscape designations and conservation, and farmland of high nature value.

resolution: 20x20
extent: whole of Scotland
crs: British National Grid
nr. features: 40

Contains values that either NAs or 1. NAs if that particular layers’ feature is NOT present in the cell, and 1 if it is.

FIPS_N.tif

Natural Features component

This includes data on land form types, soil types and slope.

resolution: 20x20
extent: whole of Scotland
crs: British National Grid
nr. features: 24

Contains values that either NAs or 1. NAs if that particular layers’ feature is NOT present in the cell, and 1 if it is.

Water.tif

Water component

This includes data on water feature types, as the presence of a lake or river.

resolution: 20x20
extent: whole of Scotland
crs: British National Grid
nr. features: 13

FIPS_I.tif

Infrastructure component

This includes data on road and track, footpaths and cycle networks.


resolution: 20x20
extent: whole of Scotland
crs: British National Grid
nr. features: 10

Landscape component

Landscape component features
nr Name Description
1 SLSRA_CP_2 Country Park
2 SLSRA_HNV_2 Designated High Nature Value (HNV) farmland
3 SLSRA_LCM_1 Alpine and subalpine grassland
4 SLSRA_LCM_2 Arable land and market gardens
5 SLSRA_LCM_3 Arctic, alpine and subalpine scrub
6 SLSRA_LCM_4 Bare field or exposed soil
7 SLSRA_LCM_5 Base-rich fens and calcareous spring mires
8 SLSRA_LCM_6 Broadleaved deciduous woodland
9 SLSRA_LCM_7 Built-up area
10 SLSRA_LCM_8 Coastal dunes and sandy shore
11 SLSRA_LCM_9 Coastal shingle
12 SLSRA_LCM_10 Dry grassland
13 SLSRA_LCM_11 Freshwater
14 SLSRA_LCM_12 Inland cliffs, rock pavements and outcrops
15 SLSRA_LCM_13 Lines of trees, small planted woodlands, early-stage woodland and coppice
16 SLSRA_LCM_14 Littoral sediment or saltmarsh
17 SLSRA_LCM_15 Mesic grassland
18 SLSRA_LCM_16 Mixed deciduous and coniferous woodland
19 SLSRA_LCM_17 Non-native coniferous plantation
20 SLSRA_LCM_18 Raised and blanket bog
21 SLSRA_LCM_19 Riverine and fen scrubs
22 SLSRA_LCM_20 Rock cliffs, ledges and shores
23 SLSRA_LCM_21 Scots pine woodland
24 SLSRA_LCM_22 Screes
25 SLSRA_LCM_23 Seasonally wet and wet grassland
26 SLSRA_LCM_24 Temperate montane scrub
27 SLSRA_LCM_25 Temperate shrub heathland
28 SLSRA_LCM_26 Valley mires, poor fens and transition mires
29 SLSRA_LCM_27 Windthrown woodland
30 SLSRA_LCM_28 Woodland fringes and clearings and tall forb stands
31 SLSRA_NNR_2 National Nature Reserve (NNR)
32 SLSRA_NP_2 National Park
33 SLSRA_NR_2 Nature Reserve
34 SLSRA_RP_2 Regional Park
35 SLSRA_RSPB_2 Royal Society for the Protection of Birds (RSPB) Reserve
36 SLSRA_SAC_2 Special Area of Conservation (SAC)
37 SLSRA_SPA_2 Special Protection Area (SPA)
38 SLSRA_SSSI_2 Site of Special Scientific Interest (SSSI)
39 SLSRA_SWT_2 Scottish Wildlife Trust Reserve
40 SLSRA_WLA_2 Wild Land Areas

Natural Features component

Natural Features component features
nr Name Description
1 FIPS_N_Landform_1 Foothills
2 FIPS_N_Landform_2 Mountains
3 FIPS_N_Landform_3 Terraces
4 FIPS_N_Landform_4 Flood plain
5 FIPS_N_Landform_5 Beaches or Dunes
6 FIPS_N_Landform_6 Rocks or Scree
7 FIPS_N_Landform_7 Depressions
8 FIPS_N_Landform_8 Hills
9 FIPS_N_Landform_9 Lowlands
10 FIPS_N_Landform_10 Rock Walls
11 FIPS_N_Landform_11 Uplands
12 FIPS_N_Landform_12 Valley sides
13 FIPS_N_Landform_13 Valley bottom
14 FIPS_N_Landform_14 Built-up areas
15 FIPS_N_Landform_15 Saltings
16 FIPS_N_Landform_16 Hummocks, mounds or moraines
17 FIPS_N_Slope_1 No slope
18 FIPS_N_Slope_2 Gentle slope
19 FIPS_N_Slope_3 Medium slope
20 FIPS_N_Slope_4 Steep slope
21 FIPS_N_Slope_5 Very steep slope
22 FIPS_N_Slope_6 Extremely steep slope
23 FIPS_N_Soil_1 Peat or Organic
24 FIPS_N_Soil_2 Mineral

Infrastructure component

Infrastructure component features
nr Name Description
1 FIPS_I_LocalPathNetwork_2 Path
2 FIPS_I_RoadsTracks_4 Minor or local road
3 FIPS_I_RoadsTracks_2 A Road
4 FIPS_I_RoadsTracks_1 Motorway
5 FIPS_I_RoadsTracks_5 Access roads or Track
6 FIPS_I_RoadsTracks_3 B Road
7 FIPS_I_NationalCycleNetwork_1 On Road: Paved Surface
8 FIPS_I_NationalCycleNetwork_2 Traffic Free: Unpaved Surface
9 FIPS_I_NationalCycleNetwork_3 Traffic Free: Paved Surface
10 FIPS_I_NationalCycleNetwork_4 On Road: Unpaved Surface

The infrastructure component is derived from a raster with weather or not the cell had or not that feature (1 = it had it; and NA= did not have it). - which is labelled “original” in the example map for Easter Bush; which CHRISTOPHER ANDREWS TO ADD INFO ABOUT IT HERE.

Then, a value = to the the distance in m its center has from the closest value named “distance” in example map, using the terra::distance function. Since this procedure is very memory consuming, the original Scotland wide raster was split into 20 “windows” and for each of them a 10km “buffer” was allowed (to make sure to not overlook the vicinity to features belonging to neighboring windows).

Then, considering only the cells that were <= 500m distance from a feature, the values were re-scaled from 0 to 1 (1 = present in cell, and then decreasing score as you get away from that cell) named “scored” in map below, using the JOE TO ADD PAPER THIS FUNCTION and kappa and alpha values COMES FROM:

\[ distance (m) = \frac{\kappa + 1}{\kappa + \exp(\alpha x)} \]

Water component

Water component features
nr Name Description
1 Water_Lakes_1 Pond
2 Water_Lakes_2 Lochan
3 Water_Lakes_3 Small Lochs
4 Water_Lakes_4 Medium Lochs
5 Water_Lakes_5 Large Lochs
6 Water_Lakes_6 Major Lochs
7 Water_Rivers_1 Minor river or tributary
8 Water_Rivers_2 Unnamed minor stream or tributary
9 Water_Rivers_3 Major river or tributary
10 Water_Rivers_4 Named minor stream or tributary
11 Water_Rivers_5 Lake
12 Water_Rivers_6 Tidal river or estuary
13 Water_Rivers_7 Canal

In the same way as infrastructure:

3. The Shiny app for the “watches” spin off

4. Running the model for the whole of Scotland

5. Lessons learned

  • using terra:: spatrasts instead of raster:: rasters has improved the performance significantly

  • pre-processing the majority of the operations on the scotland wide component rasters (uniforming their extent, resolution, projection, pre calculating the distance, and score etc.) incredibly reduces the run time.

6. Future improvements

  • less features o score, merge features into bigger categories

  • link the model to “alive” data set” that gets updated regularly to generate up to date original raster layers

  • at the moment all four components weight the same, there can be an option to have them weight differently, as the user specifies.